Advanced analytics through FPGA based query processing and deep reinforcement learning

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics.Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem.Postprint (published version

Automated Debugging Methodology for FPGA-based Systems

Electronic devices make up a vital part of our lives. These are seen from mobiles, laptops, computers, home automation, etc. to name a few. The modern designs constitute billions of transistors. However, with this evolution, ensuring that the devices fulfill the designer’s expectation under variable conditions has also become a great challenge. This requires a lot of design time and effort. Whenever an error is encountered, the process is re-started. Hence, it is desired to minimize the number of spins required to achieve an error-free product, as each spin results in loss of time and effort. Software-based simulation systems present the main technique to ensure the verification of the design before fabrication. However, few design errors (bugs) are likely to escape the simulation process. Such bugs subsequently appear during the post-silicon phase. Finding such bugs is time-consuming due to inherent invisibility of the hardware. Instead of software simulation of the design in the pre-silicon phase, post-silicon techniques permit the designers to verify the functionality through the physical implementations of the design. The main benefit of the methodology is that the implemented design in the post-silicon phase runs many order-of-magnitude faster than its counterpart in pre-silicon. This allows the designers to validate their design more exhaustively. This thesis presents five main contributions to enable a fast and automated debugging solution for reconfigurable hardware. During the research work, we used an obstacle avoidance system for robotic vehicles as a use case to illustrate how to apply the proposed debugging solution in practical environments. The first contribution presents a debugging system capable of providing a lossless trace of debugging data which permits a cycle-accurate replay. This methodology ensures capturing permanent as well as intermittent errors in the implemented design. The contribution also describes a solution to enhance hardware observability. It is proposed to utilize processor-configurable concentration networks, employ debug data compression to transmit the data more efficiently, and partially reconfiguring the debugging system at run-time to save the time required for design re-compilation as well as preserve the timing closure. The second contribution presents a solution for communication-centric designs. Furthermore, solutions for designs with multi-clock domains are also discussed. The third contribution presents a priority-based signal selection methodology to identify the signals which can be more helpful during the debugging process. A connectivity generation tool is also presented which can map the identified signals to the debugging system. The fourth contribution presents an automated error detection solution which can help in capturing the permanent as well as intermittent errors without continuous monitoring of debugging data. The proposed solution works for designs even in the absence of golden reference. The fifth contribution proposes to use artificial intelligence for post-silicon debugging. We presented a novel idea of using a recurrent neural network for debugging when a golden reference is present for training the network. Furthermore, the idea was also extended to designs where golden reference is not present

Advanced analytics through FPGA based query processing and deep reinforcement learning

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics.Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem

Scalable String and Suffix Sorting: Algorithms, Techniques, and Tools

This dissertation focuses on two fundamental sorting problems: string sorting and suffix sorting. The first part considers parallel string sorting on shared-memory multi-core machines, the second part external memory suffix sorting using the induced sorting principle, and the third part distributed external memory suffix sorting with a new distributed algorithmic big data framework named Thrill.Comment: 396 pages, dissertation, Karlsruher Instituts f\"ur Technologie (2018). arXiv admin note: text overlap with arXiv:1101.3448 by other author

A Framework for Facilitating Secure Design and Development of IoT Systems

The term Internet of Things (IoT) describes an ever-growing ecosystem of physical objects or things interconnected with each other and connected to the Internet. IoT devices consist of a wide range of highly heterogeneous inanimate and animate objects. Thus, a thing in the context of the IoT can even mean a person with blood pressure or heart rate monitor implant or a pet with a biochip transponder. IoT devices range from ordinary household appliances, such as smart light bulbs or smart coffee makers, to sophisticated tools for industrial automation. IoT is currently leading a revolutionary change in many industries and, as a result, a lot of industries and organizations are adopting the paradigm to gain a competitive edge. This allows them to boost operational efficiency and optimize system performance through real-time data management, which results in an optimized balance between energy usage and throughput. Another important application area is the Industrial Internet of Things (IIoT), which is the application of the IoT in industrial settings. This is also referred to as the Industrial Internet or Industry 4.0, where Cyber- Physical Systems (CPS) are interconnected using various technologies to achieve wireless control as well as advanced manufacturing and factory automation. IoT applications are becoming increasingly prevalent across many application domains, including smart healthcare, smart cities, smart grids, smart farming, and smart supply chain management. Similarly, IoT is currently transforming the way people live and work, and hence the demand for smart consumer products among people is also increasing steadily. Thus, many big industry giants, as well as startup companies, are competing to dominate the market with their new IoT products and services, and hence unlocking the business value of IoT. Despite its increasing popularity, potential benefits, and proven capabilities, IoT is still in its infancy and fraught with challenges. The technology is faced with many challenges, including connectivity issues, compatibility/interoperability between devices and systems, lack of standardization, management of the huge amounts of data, and lack of tools for forensic investigations. However, the state of insecurity and privacy concerns in the IoT are arguably among the key factors restraining the universal adoption of the technology. Consequently, many recent research studies reveal that there are security and privacy issues associated with the design and implementation of several IoT devices and Smart Applications (smart apps). This can be attributed, partly, to the fact that as some IoT device makers and smart apps development companies (especially the start-ups) reap business value from the huge IoT market, they tend to neglect the importance of security. As a result, many IoT devices and smart apps are created with security vulnerabilities, which have resulted in many IoT related security breaches in recent years. This thesis is focused on addressing the security and privacy challenges that were briefly highlighted in the previous paragraph. Given that the Internet is not a secure environ ment even for the traditional computer systems makes IoT systems even less secure due to the inherent constraints associated with many IoT devices. These constraints, which are mainly imposed by cost since many IoT edge devices are expected to be inexpensive and disposable, include limited energy resources, limited computational and storage capabilities, as well as lossy networks due to the much lower hardware performance compared to conventional computers. While there are many security and privacy issues in the IoT today, arguably a root cause of such issues is that many start-up IoT device manufacturers and smart apps development companies do not adhere to the concept of security by design. Consequently, some of these companies produce IoT devices and smart apps with security vulnerabilities. In recent years, attackers have exploited different security vulnerabilities in IoT infrastructures which have caused several data breaches and other security and privacy incidents involving IoT devices and smart apps. These have attracted significant attention from the research community in both academia and industry, resulting in a surge of proposals put forward by many researchers. Although research approaches and findings may vary across different research studies, the consensus is that a fundamental prerequisite for addressing IoT security and privacy challenges is to build security and privacy protection into IoT devices and smart apps from the very beginning. To this end, this thesis investigates how to bake security and privacy into IoT systems from the onset, and as its main objective, this thesis particularly focuses on providing a solution that can foster the design and development of secure IoT devices and smart apps, namely the IoT Hardware Platform Security Advisor (IoT-HarPSecA) framework. The security framework is expected to provide support to designers and developers in IoT start-up companies during the design and implementation of IoT systems. IoT-HarPSecA framework is also expected to facilitate the implementation of security in existing IoT systems. To accomplish the previously mentioned objective as well as to affirm the aforementioned assertion, the following step-by-step problem-solving approach is followed. The first step is an exhaustive survey of different aspects of IoT security and privacy, including security requirements in IoT architecture, security threats in IoT architecture, IoT application domains and their associated cyber assets, the complexity of IoT vulnerabilities, and some possible IoT security and privacy countermeasures; and the survey wraps up with a brief overview of IoT hardware development platforms. The next steps are the identification of many challenges and issues associated with the IoT, which narrowed down to the abovementioned fundamental security/privacy issue; followed by a study of different aspects of security implementation in the IoT. The remaining steps are the framework design thinking process, framework design and implementation, and finally, framework performance evaluation. IoT-HarPSecA offers three functionality features, namely security requirement elicitation security best practice guidelines for secure development, and above all, a feature that recommends specific Lightweight Cryptographic Algorithms (LWCAs) for both software and hardware implementations. Accordingly, IoT-HarPSecA is composed of three main components, namely Security Requirements Elicitation (SRE) component, Security Best Practice Guidelines (SBPG) component, and Lightweight Cryptographic Algorithms Recommendation (LWCAR) component, each of them servicing one of the aforementioned features. The author has implemented a command-line tool in C++ to serve as an interface between users and the security framework. This thesis presents a detailed description, design, and implementation of the SRE, SBPG, and LWCAR components of the security framework. It also presents real-world practical scenarios that show how IoT-HarPSecA can be used to elicit security requirements, generate security best practices, and recommend appropriate LWCAs based on user inputs. Furthermore, the thesis presents performance evaluation of the SRE, SBPG, and LWCAR components framework tools, which shows that IoT-HarPSecA can serve as a roadmap for secure IoT development.O termo Internet das coisas (IoT) é utilizado para descrever um ecossistema, em expansão, de objetos físicos ou elementos interconetados entre si e à Internet. Os dispositivos IoT consistem numa gama vasta e heterogénea de objetos animados ou inanimados e, neste contexto, podem pertencer à IoT um indivíduo com um implante que monitoriza a frequência cardíaca ou até mesmo um animal de estimação que tenha um biochip. Estes dispositivos variam entre eletrodomésticos, tais como máquinas de café ou lâmpadas inteligentes, a ferramentas sofisticadas de uso na automatização industrial. A IoT está a revolucionar e a provocar mudanças em várias indústrias e muitas adotam esta tecnologia para incrementar as suas vantagens competitivas. Este paradigma melhora a eficiência operacional e otimiza o desempenho de sistemas através da gestão de dados em tempo real, resultando num balanço otimizado entre o uso energético e a taxa de transferência. Outra área de aplicação é a IoT Industrial (IIoT) ou internet industrial ou Indústria 4.0, ou seja, uma aplicação de IoT no âmbito industrial, onde os sistemas ciberfísicos estão interconectados a diversas tecnologias de forma a obter um controlo de rede sem fios, bem como fabricações avançadas e automatização fabril. As aplicações da IoT estão a crescer e a tornarem-se predominantes em muitos domínios de aplicação inteligentes como sistemas de saúde, cidades, redes, agricultura e sistemas de fornecimento. Da mesma forma, a IoT está a transformar estilos de vida e de trabalho e assim, a procura por produtos inteligentes está constantemente a aumentar. As grandes indústrias e startups competem entre si de forma a dominar o mercado com os seus novos serviços e produtos IoT, desbloqueando o valor de negócio da IoT. Apesar da sua crescente popularidade, benefícios e capacidades comprovadas, a IoT está ainda a dar os seus primeiros passos e é confrontada com muitos desafios. Entre eles, problemas de conectividade, compatibilidade/interoperabilidade entre dispositivos e sistemas, falta de padronização, gestão das enormes quantidades de dados e ainda falta de ferramentas para investigações forenses. No entanto, preocupações quanto ao estado de segurança e privacidade ainda estão entre os fatores adversos à adesão universal desta tecnologia. Estudos recentes revelaram que existem questões de segurança e privacidade associadas ao design e implementação de vários dispositivos IoT e aplicações inteligentes (smart apps.), isto pode ser devido ao facto, em parte, de que alguns fabricantes e empresas de desenvolvimento de dispositivos (especialmente startups) IoT e smart apps., recolham o valor de negócio dos grandes mercados IoT, negligenciando assim a importância da segurança, resultando em dispositivos IoT e smart apps. com carências e violações de segurança da IoT nos últimos anos. Esta tese aborda os desafios de segurança e privacidade que foram supra mencionados. Visto que a Internet e os sistemas informáticos tradicionais são por vezes considerados inseguros, os sistemas IoT tornam-se ainda mais inseguros, devido a restrições inerentes a tais dispositivos. Estas restrições são impostas devido ao custo, uma vez que se espera que muitos dispositivos de ponta sejam de baixo custo e descartáveis, com recursos energéticos limitados, bem como limitações na capacidade de armazenamento e computacionais, e redes com perdas devido a um desempenho de hardware de qualidade inferior, quando comparados com computadores convencionais. Uma das raízes do problema é o facto de que muitos fabricantes, startups e empresas de desenvolvimento destes dispositivos e smart apps não adiram ao conceito de segurança por construção, ou seja, logo na conceção, não preveem a proteção da privacidade e segurança. Assim, alguns dos produtos e dispositivos produzidos apresentam vulnerabilidades na segurança. Nos últimos anos, hackers maliciosos têm explorado diferentes vulnerabilidades de segurança nas infraestruturas da IoT, causando violações de dados e outros incidentes de privacidade envolvendo dispositivos IoT e smart apps. Estes têm atraído uma atenção significativa por parte das comunidades académica e industrial, que culminaram num grande número de propostas apresentadas por investigadores científicos. Ainda que as abordagens de pesquisa e os resultados variem entre os diferentes estudos, há um consenso e pré-requisito fundamental para enfrentar os desafios de privacidade e segurança da IoT, que buscam construir proteção de segurança e privacidade em dispositivos IoT e smart apps. desde o fabrico. Para esta finalidade, esta tese investiga como produzir segurança e privacidade destes sistemas desde a produção, e como principal objetivo, concentra-se em fornecer soluções que possam promover a conceção e o desenvolvimento de dispositivos IoT e smart apps., nomeadamente um conjunto de ferramentas chamado Consultor de Segurança da Plataforma de Hardware da IoT (IoT-HarPSecA). Espera-se que o conjunto de ferramentas forneça apoio a designers e programadores em startups durante a conceção e implementação destes sistemas ou que facilite a integração de mecanismos de segurança nos sistemas préexistentes. De modo a alcançar o objetivo proposto, recorre-se à seguinte abordagem. A primeira fase consiste num levantamento exaustivo de diferentes aspetos da segurança e privacidade na IoT, incluindo requisitos de segurança na arquitetura da IoT e ameaças à sua segurança, os seus domínios de aplicação e os ativos cibernéticos associados, a complexidade das vulnerabilidades da IoT e ainda possíveis contramedidas relacionadas com a segurança e privacidade. Evolui-se para uma breve visão geral das plataformas de desenvolvimento de hardware da IoT. As fases seguintes consistem na identificação dos desafios e questões associadas à IoT, que foram restringidos às questões de segurança e privacidade. As demais etapas abordam o processo de pensamento de conceção (design thinking), design e implementação e, finalmente, a avaliação do desempenho. O IoT-HarPSecA é composto por três componentes principais: a Obtenção de Requisitos de Segurança (SRE), Orientações de Melhores Práticas de Segurança (SBPG) e a recomendação de Componentes de Algoritmos Criptográficos Leves (LWCAR) na implementação de software e hardware. O autor implementou uma ferramenta em linha de comandos usando linguagem C++ que serve como interface entre os utilizadores e a IoT-HarPSecA. Esta tese apresenta ainda uma descrição detalhada, desenho e implementação das componentes SRE, SBPG, e LWCAR. Apresenta ainda cenários práticos do mundo real que demostram como o IoT-HarPSecA pode ser utilizado para elicitar requisitos de segurança, gerar boas práticas de segurança (em termos de recomendações de implementação) e recomendar algoritmos criptográficos leves apropriados com base no contributo dos utilizadores. De igual forma, apresenta-se a avaliação do desempenho destes três componentes, demonstrando que o IoT-HarPSecA pode servir como um roteiro para o desenvolvimento seguro da IoT

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

Engines of Order

Over the last decades, and in particular since the widespread adoption of the Internet, encounters with algorithmic procedures for ‘information retrieval’ – the activity of getting some piece of information out of a col-lection or repository of some kind – have become everyday experiences for most people in large parts of the world

Whole genome doubling propagates chromosomal instability and accelerates cancer genome evolution

Tetraploidy has long been proposed as an intermediate cellular stage en route to the aneuploidy and chromosomal instability that is observed in many cancer types. Although tetraploidy has been shown to be an unstable cellular state, an in depth analysis of the effect of a spontaneous tetraploidisation event on the cancer genome has not been carried out. Using an isogenic system of naturally occurring tetraploid cells derived from a chromosomally stable colorectal cancer cell line, the effect of tetraploidisation on genome stability over time was assessed. Tetraploid cells were shown to have increased structural and numerical instability on a per cell but not per chromosome basis. Over time the tetraploid genome became increasingly genomically unstable, which is likely due to the increased ability of tetraploid clones to propagate segregation errors. The genomic landscape of tetraploid clones began to recapitulate the genomic architecture of chromosomally unstable colorectal cancer, and allowed for the selection of clinically high risk chromosomal losses over time. Genome doubling was further shown to be a predictive marker of poor prognosis in colorectal cancer. Exhaustive analysis of DNA and mRNA failed to reveal any common changes in tetraploid clones that are likely to explain their aneuploidy tolerance phenotype. Instead a focussed siRNA screen of genes commonly mutated in genome-doubled tumours of multiple cancer types was carried out. Given the high-risk clinical phenotypes associated with tetraploidy and chromosomal instability, it remains a priority to identify the mechanisms allowing tumour cells to undergo tetraploidisation and to sustain chromosome segregation errors

Reports to the President

A compilation of annual reports for the 1986-1987 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans